Scene Reconstruction via Tensor Field Interpolation
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Solution Overview
Problem
Conventional scene reconstruction technologies using RGB-D cameras are limited to individual object scanning or fixed camera setups, restricting the reconstructible space dimension and scalability, especially in complex environments.
Innovation Solution
A method involving spatial-temporal interpolation of tensor field key frames and particle advection algorithms to plan robot and camera paths, enabling efficient exploration and reconstruction of complex scenes by a robot equipped with an RGB-D camera, optimizing path planning to ensure comprehensive coverage and reconstruction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If conventional individual object scanning or fixed camera scene exploration is used, then the reconstruction process is simple, but the reconstructible space dimension is restricted
Solution Approach 1:
The patent implements dynamic path planning for both the robot and camera by computing optimal trajectories that adapt to the scene geometry. The robot follows a planned path while the camera captures depth images at multiple poses along its trajectory, enabling exploration of larger and more complex spaces compared to fixed camera setups.
Solution Approach 2:
The patent employs a hierarchical structure where the robot system contains a nested camera system. The robot moves along a planned path while carrying a camera that simultaneously moves along its own planned path, creating a nested exploration architecture that multiplies the effective reconstruction capability.
2Manufacturing precision
If complex scenes are explored with multiple key frames, then the reconstruction quality improves, but the path planning becomes more difficult
Solution Approach 1:
The patent segments the exploration task into multiple key frames that are interpolated to form complete paths. The tensor field is computed at discrete key frames and then interpolated to generate continuous paths for both robot and camera, breaking down the complex planning problem into manageable segments.
Solution Approach 2:
The patent performs preliminary computation of tensor fields at key frames before generating the actual paths. By pre-computing the tensor field structure at strategically chosen key frames and interpolating between them, the system simplifies the overall path planning while ensuring reconstruction quality at critical points.
3Stability of the object's composition
If spatial-temporal interpolation is performed between key frames, then the path smoothness improves, but the computational time increases
Solution Approach 1:
The patent applies spatial-temporal interpolation selectively rather than uniformly across all possible paths. By interpolating tensor fields at key frames and generating paths only where needed, the system achieves sufficient path smoothness without the computational overhead of exhaustive interpolation throughout the entire exploration space.
Data Source
AI summary
A method of scene reconstruction includes: a depth image taken by a camera mounted on a robot is acquired, and a current tensor field key frame is generated based on the depth image; spatial-temporal interpolation is performed on the current tensor field key frame and a previous tensor field key frame to obtain an interpolated tensor field key frame; a robot planning path is acquired by particle advection algorithm according to the current tensor field key frame, the previous tensor field key frame, and the interpolated tensor field key frame; a camera planning path is acquired according to the robot planning path and an energy equation of the camera; and a scene is reconstructed according to the depth images captured by the camera.